Many organizations rely upon data replication to improve the reliability, fault-tolerance, and/or accessibility of their applications and/or data. Data replication typically involves replicating data from a primary site or device (such as an application server) to a secondary (i.e., backup) site or device.
Sometimes an organization may run an application on a cluster of nodes. In order to replicate the data of a clustered application, each node of the cluster may replicate write operations issued from that node. However, allowing each node to replicate its write operations in an arbitrary order (e.g., without regard to replicated write operations issued from other nodes) may result in an invalid replica. On the other hand, enforcing a strict serialization on the replication of the write operations may slow down the replication process to the point that the state of the replication cannot keep up with the state of the primary application data.
Traditional technologies may attempt to improve the efficiency of replication in a clustered environment by distinguishing between dependent and independent write operations. If two write operations are mutually independent, then they may be applied in any order with the same result. Accordingly, traditional technologies may transfer dependency relationship data between nodes in order to ensure global write order fidelity. Unfortunately, transferring the necessary dependency relationship data may consume a substantial amount of computing resources and may potentially degrade the performance of the primary application. Furthermore, this traditional method may scale poorly with the size of the cluster. Consequently, the present disclosure identifies a need for additional and improved methods for replicating data in cluster environments.